BMJ Open. Secondary Subject Heading: General practice / Family practice, Cardiovascular medicine

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1 BMJ Open What is the relationship between renal function and visitto-visit blood pressure variability in primary care? Retrospective cohort study from routinely collected healthcare data. Journal: BMJ Open Manuscript ID bmjopen Article Type: Research Date Submitted by the Author: -Nov-0 Complete List of Authors: Lasserson, Daniel; Oxford University Hospitals NHS Foundation Trust, NIHR Oxford Biomedical Research Centre; University of Oxford, Nuffield Department of Medicine Scherpbier de Haan, Nynke; Radboud University Nijmegen Medical Centre, Primary and Community Care de Grauw, Wim; Radboud University Nijmegen Medical Centre, Primary and Community Care van der Wel, Mark; Radboud University Nijmegen Medical Centre, Primary and Community Care Wetzels, Jack; Radboud University Nijmegen Medical Centre, Nephrology O'Callaghan, Christopher; University of Oxford, Nuffield Department of Clinical Medicine; Oxford Radcliffe Hospitals Trust, John Radcliffe Hospital <b>primary Subject Heading</b>: Renal medicine Secondary Subject Heading: General practice / Family practice, Cardiovascular medicine Keywords: Blood Pressure, Chronic kidney disease, Variability BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

2 Page of BMJ Open What is the relationship between renal function and visit-to-visit blood pressure variability in primary care? Retrospective cohort study from routinely collected healthcare data. Daniel S. Lasserson MBBS MD,, Nynke Scherpbier de Haan MD PhD, Wim de Grauw MD PhD, Mark van der Wel MD PhD, Jack F. Wetzels MD PhD, Christopher A. O Callaghan MB BChir DM DPhil Nuffield Department of Medicine, University of Oxford, UK NIHR Oxford Biomedical Research Centre, Department of Geratology, Oxford University Hospitals NHS Trust, Oxford, UK Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, Netherlands Department of Nephrology, Radboud University Nijmegen Medical Centre, Netherlands Corresponding author: Professor Christopher O Callaghan, Henry Wellcome Building for Molecular Physiology, Nuffield Department of Medicine, Roosevelt Drive, University of Oxford, Oxford OX B Tel + Fax + chris.ocallaghan@ndm.ox.ac.uk Word Count 0 Keywords Blood pressure, chronic kidney disease, variability. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

3 BMJ Open Page of ABSTRACT Objective: To determine the relationship between renal function and visit to visit blood pressure variability in a cohort of primary care patients. Design: Retrospective cohort study from routinely collected healthcare data. Setting: Primary care in Nijmegen, Netherlands from Participants:, patients who had a measure of renal function and seven separate visits with blood pressure readings in the primary care record Outcome measures: Visit-to-visit variability in systolic blood pressure (BP), calculated from the first seven office measurements, including standard deviation, successive variation, absolute real variation and metrics of variability shown to be independent of mean. Multiple linear regression was used to analyze the influence of estimated glomerular filtration rate (egfr) on BP variability measures with adjustment for age, sex, diabetes, mean BP, proteinuria, cardiovascular disease, time interval between measures and antihypertensive use. Results: In the patient cohort, % were female, mean (SD) age was. (.) years, mean (SD) egfr was. (.0) ml/min/.m and mean (SD) systolic BP. (.) mmhg. All BP variability measures were negatively correlated with egfr and positively correlated with age. However, multiple linear regression demonstrated consistent, significant negative relationships between egfr and all measures of BP variability adjusting for confounding variable. Conclusion: Worsening renal function is associated with small increases in measures of visit-to-visit BP variability after adjustment for confounding factors. This is seen across the spectrum of renal function in the population and provides a mechanism whereby CKD may raise the risk of cardiovascular events. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

4 Page of BMJ Open Article Summary Strengths and limitations of this study We studied BP variability in a patient cohort generalizable to primary care where the majority of patients with CKD are diagnosed and monitored as previously BP variability has only been studied in patients with advanced renal disease under specialist care. We examined measures of BP variability that are independent of mean BP, which have been shown to have greater prognostic significance than mean BP alone. We were not able to assess the effect of different exposures to antihypertensive medication. We did not have access to data on cardiovascular outcomes to test the clinical significance of visit-to-visit variability in this patient cohort. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

5 BMJ Open Page of Introduction Hypertension is a major vascular risk factor and average blood pressure (BP) correlates with cardiovascular disease and death. Recently, visit-to-visit variability in BP measurements has been shown to convey an additional independent risk for cardiovascular events and to be associated with all-cause mortality in the general population., BP variability is reproducible within individuals over time in clinical trials and in observational data from routine care. The cardiovascular prognostic significance of BP variability is increasingly recognized and the variability is not explained by poor medication adherence. Increased BP variability has been observed in small groups of patients with relatively advanced renal disease under specialist hospital care. In a small hospital cohort, visit-to-visit BP variability was independently associated with albuminuria and with increased renal vascular resistance as assessed by Doppler ultrasound. A study of patients with non-diabetic chronic kidney disease (CKD) indicated a correlation between indices of BP variability and the rate of decline in renal function. In type diabetes, visit-to-visit variability in systolic BP correlates with the level of albuminuria, ankle brachial pressure index and the probability of developing new albuminuria. 0 In a set of secondary care patients with substantial renal impairment, there was a correlation between BP variability and a composite outcome of death and cardiovascular events. In patients on haemodialysis, visit-to-visit variability in BP is an independent predictor of vascular events, but this is complicated by the dependence of BP in this context on intradialytic fluid gain. However, as BP variability increases with mean BP and renal disease is associated with increased mean BP, the relationship between BP variability and renal impairment can only be established using metrics of BP variability that have been shown to be independent of mean BP. None of the renal studies above reported statistics of BP variability that were demonstrated to be independent of the - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

6 Page of BMJ Open mean BP and, therefore, an evidence gap remains concerning whether declining renal function is associated with BP variability. It is important to establish whether there is a real relationship between renal impairment and increased BP variability because the link might be causal and have important implications for therapy in CKD. Analysis of large sets of routinely collected healthcare data has demonstrated that CKD is an important independent risk factor for cardiovascular disease even after controlling for other known risk factors., The prevalence of CKD is around -0 % and the majority of patients with CKD are managed by non-specialists or primary care physicians., Given that a substantial proportion of the population have early stage CKD, it is therefore crucial to establish whether early stage CKD is associated with increased BP variability, as there could be major implications for population cardiovascular risk factor monitoring and treatment. We therefore sought to determine whether renal function was associated with visit-to-visit BP variability using a large dataset of routinely collected healthcare data. Methods We retrospectively defined a patient cohort (age years) using routinely collected healthcare data from primary care practices in the Nijmegen region, the Netherlands, with a combined registered population of 0, people. Data included demographic details, medical history with conditions diagnosed in the course of routine clinical practice,bp measurements, antihypertensive prescribing and renal function between st January 00 and st January 0. We did not seek to include patients receiving dialysis in this study population. The CKD-EPI formula was used to calculate estimated glomerular filtration rates (egfrs) using the first creatinine measurements in the time period, which were either standardized to isotope dilution-mass spectrometry (IDMS) or subject to the appropriate correction factor for laboratories using the Jaffé technique. CKD-EPI - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

7 BMJ Open Page of egfrs have been shown to correlate better with measured GFRs than egfrs obtained with the MDRD formula. The first seven office BP measurements were used to calculate variability metrics as this approach has been shown to optimize reproducibility. Dutch primary care physicians and practice nurses have free access to the Dutch College of General Practitioners guideline on cardiovascular risk management for a clear description of standardised office BP measurement, which is in agreement with international standards. Given that blood pressure variability is associated with mean BP, we calculated BP variability independent of mean BP using standard formulae for measures that have prognostic significance for cardiovascular events. For the systolic and diastolic BP of individuals with at least seven BP measures we calculated the standard deviation (SD), successive variation (SV), absolute residual variation (ARV) and transformed these to be independent of mean BP (see supplementary information for formulae). Observed associations between renal functionand BP variability (systolic BP metrics described above and also including maximum systolic BP) may be confounded by other predictors of variability and we therefore tested the association between renal function and measures of BP variability using multiple stepwise linear regression adjusting for age, sex, mean BP, diabetes, history of cardiovascular disease (including vascular events, arrhythmia and heart failure), proteinuria, time interval between BP measures and class of antihypertensive drug prescribed. We included mean BP as a covariate to ensure that the effect of mean BP was truly adjusted for in regressions testing the relationship between variability independent of mean (VIM) statistics and egfr. Analyses were carried out using R (version.0.) and SPSS version. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

8 Page of BMJ Open Results Among the 0, total registered population,, were over the age of, of whom,0 (%) had renal function measured during the study period. Of these,, (0%) had at least seven BP measurements recorded at different visits. Table lists the features of the group which had a mean age of. years, a mean blood creatinine concentration of. micromol/l (0.mg/dL), a mean egfr of.ml/min/.m and a mean systolic BP of. mmhg. Table Demographics of population studied (n =,) Age. (.) years % Female % CKD-EPI egfr. (.0) ml/min/.m Creatinine. (.0) micromol/l or 0. (0.) mg/dl Diagnosed with diabetes.% Number of antihypertensive drugs 0.% prescribed (proportion of patients).% 0.%.%.%.0% 0.% Interval between st and th BP measure () days Systolic BP at baseline. (.) mmhg Continuous data are presented as mean (standard deviation). The mean time interval from the first to the final seventh BP measurement was months (SD months). Mean systolic BP rose with age (R value 0., p<0.00) and with falling egfr (R= -0., p<0.00). Correlation analysis of transformed BP variability measures was consistent with their independence from mean BP (R values; standard deviation independent of mean (SDIM) -0.00, p=0.; successive variation independent of mean (SVIM) -0.00, p=0.; average real variability independent of mean (ARVIM) -0.0, p=0.). - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

9 BMJ Open Page of Table shows the correlations between mean systolic BP and its variability with deteriorating egfr and with increasing age. All measures of intra-individual BP variability increase progressively with declining egfr and with increasing age. Table correlation of systolic BP metrics with egfr and with age BP metric Correlation with egfr Correlation with age Mean systolic BP -0.0 *** 0.0 *** Maximum systolic BP -0. *** 0. *** Standard deviation of -0. *** 0. *** systolic BP Successive Variation -0. *** 0. *** (SV) of systolic BP Absolute real -0. *** 0. *** variation (ARV) of systolic BP SD independent of -0.0 *** 0.0 *** mean BP (SDIM) SV independent of -0.0 *** 0.0 *** mean BP (SDIM) ARV independent of mean BP (ARVIM) -0.0 *** 0.0 *** *** P<0.00 The relationships of egfr and age with SDIM, as an exemplar of the associations with variability independent of mean, are shown in Figures A and B. Multivariate stepwise regression analysis was performed to examine the association between renal function and BP variability. Table shows the standardised beta coefficients and associated p values from multivariate regression analyses of egfr on BP variability metrics using, age, sex, mean systolic BP, diabetes, cardiovascular disease, proteinuria, interval between BP measures and the class of antihypertensive drug prescribed as covariates (medication classes are not shown, see Supplementary Material). - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

10 Page of BMJ Open Table. Standardised beta coefficients and associated p values from multivariate linear regressions of egfr on measures of BP variability adjusted for potential confounders (medication classes not shown) Measure of BP Variability egfr Std beta age Std beta Sex Std beta Mean BP Std beta Vascular disease Std beta BP interval Std beta Diabetes Std beta SD -0.0 *** *** 0. *** 0.0 *** *** ARV -0.0 *** 0.0 *** 0.0 *** 0. *** 0.0 *** 0.0 * -0.0 *** SRV -0.0 *** 0.0 * 0.0 *** 0. *** 0.0 *** 0.0 * -0.0 *** Max -0.0 *** -0.0 * 0.0 *** 0. *** 0.0 *** *** systolic SDIM -0.0 *** *** -0.0 *** 0.0 *** *** ARVIM -0.0 *** 0.0 * 0.0 *** -0.0 *** 0.0 *** 0.0 * -0.0 *** SRVIM -0.0 *** 0.0 ** 0.0 *** -0.0 *** 0.0 *** 0.0 * -0.0 *** * P<0.0, ** P<0.0, *** P<0.00 Lower egfr was consistently associated with increasing measures of BP variability (both with and without transformation to be independent of mean BP), as evidenced by the significant negative standardised beta coefficients across the regressions in the presence of potential confounders. Although the presence of proteinuria was not associated with any measure of BP variability, a history of cardiovascular disease was positively associated with increased BP variability. All classes of antihypertensive medication were positively associated with measures of BP variability where significant relationships were observed (see Supplementary Material) Greater BP variability was associated with longer time intervals between BP measures, as evidenced by the positive coefficients where significant relationships were observed. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

11 BMJ Open Page 0 of Discussion Both mean BP and visit-to-visit BP variability increased with age and with worsening renal function in this large community-based population. Further analysis with multivariate regression demonstrated that worsening renal function remains significantly associated with increased BP variability independent of mean, even after adjustment for age, sex, diabetes, history of cardiovascular disease, mean systolic blood pressure, interval between BP measures, proteinuria and the class of antihypertensive drug prescribed. This is important because visit-to-visit BP variability over the medium term is a strong predictor of cardiovascular outcome independently of mean BP. In addition, this relationship between renal function and BP variability may explain, in part, the association between renal function and cardiovascular disease, which remains even after controlling for other established cardiovascular risk factors. Hitherto the relationship between renal disease and BP variability has not been studied in large unselected community-based populations. In specialist services which typically care for patients with advanced renal disease or those requiring renal replacement therapy, visit-to-visit variability in BP has been linked to poorer renal and cardiovascular outcomes. 0 In settings, such as the UK or Netherlands, where guidelines exist for specialist referral,, there is substantial enrichment in specialist service populations for advanced CKD stage, rapidly declining renal function or significant proteinuria. Observational studies of these groups for prognostic factors has an uncertain generalizability to the much larger, but less severely affected, population monitored predominantly by non-specialists or primary care physicians. Furthermore, none of the previous studies of renal disease have used metrics of BP variability that have been shown to be independent of mean BP and so do not clearly distinguish between the effects of raised systolic BP and BP variability. The results of the regression analyses showed a consistent positive relationship between the presence of cardiovascular disease and BP variability, a finding that is consistent with the known BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

12 Page of BMJ Open prognostic significance of BP variability. Importantly, the time interval between BP measures was positively associated with BP variability, indicating that longer durations between measures were associated with greater variability. This counters the hypothesis that associations between greater BP variability and reduced renal function are an artefact of acute illness with episodes of acute kidney injury (AKI) where clinical monitoring will be more frequent, or due to increased monitoring at the time of initiation of antihypertensive drugs. If AKI or the initiation of antihypertensive therapy were responsible for increasing BP variability, one would expect greater variability to be associated with shorter time intervals between BP measures in a period of clinical instability or medication change. A limited post-hoc analysis of around 000 patients selected for the ASCOT-BPLA trial showed a weak relationship between blood creatinine and BP variability, but the trial design undermines the generalizability of this analysis to representative patient populations. The study excluded anyone with a creatinine of >00 micromol/l (. mg/dl), with clinically important renal disease, with secondary hypertension (which could include renal disease) or any concomitant disease requiring calcium channel blockers, ACE inhibitors, beta blockers or diuretics. These criteria would likely exclude the majority of patients with chronic kidney disease. Further, the study excluded anyone < years of age, egfrs were not reported and it is unclear whether creatinine assays were standardized to IDMS values. Creatinine is influenced by both age and renal function in part because of age-related changes in muscle mass and the analysis was weakened in this older trial population by the comparison of creatinine values across different ages. The relationship we observed between CKD and bp variability in a wider population demonstrates that, even in the early stages of renal dysfunction, BP variability is present at a level associated with significant cardiovascular risk. The measures of variability (ARV, SV and those transformed to be independent of mean) in egfr ranges corresponding to CKD stage in this study are similar to those seen in cohorts of patients with TIA in the UK-TIA trial and the European Carotid Surgery Trial. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

13 BMJ Open Page of Furthermore the timeframe of BP variability measurement in this study (mean time between BP measures of 0 days) is consistent with the time frames between repeat BP measures during follow up in the trials establishing increased BP variability and its prognostic significance., Our study has some limitations. The population studied exists in a healthcare context where the value of treating hypertension is recognized and this is likely to reduce the slope of the relationship between mean systolic BP and egfr. In addition, the variability associated with the estimation of renal function, and intrinsic error will result in regression dilution bias. However, this will bias the study towards the null hypothesis, such that the strength of the relationship between renal function and BP variability will be underestimated rather than overestimated. Error associated with the measurement of BP variability will widen the confidence limits associated with the relationship. Furthermore while our adjustment for antihypertensive use included drug-class specific effects we were not able to assess the effect of exposure by examining drop in and drop out of treatment, but given that variability may be affected by some drugs more than others, this constraint is also likely to reduce rather than enhance the associations that we have found. Nevertheless, despite these limitations we have found consistent and significant associations between measures of renal function and BP variability. Our results demonstrate that the relationship between egfr and BP variability exists across the spectrum of egfr which corresponds to different stages of CKD. This relationship is consistent with a causative relationship between diminished egfr and BP variability, but causation cannot be established in an observational study. Nevertheless, this may prove to have clinical utility in determining vascular risk and prognosis as we have demonstrated that BP variability is readily detectable and quantifiable in retrospective data in the non-specialist setting. Studies of the prognostic significance of BP variability in general CKD populations are required as monitoring BP variability may refine the prediction of cardiovascular risk. The correlations that we - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

14 Page of BMJ Open have identified may seem of relatively small magnitude, but they are independent and highly significant and given the population prevalence of CKD, this correlation will have major consequences for cardiovascular risk at the population level. Clinical trials are needed in CKD to establish the value or otherwise for renal and cardiovascular outcomes of tailoring antihypertensive therapy to minimize BP variability. Sources of Funding: Dutch Kidney Foundation. DSL is supported by the NIHR Oxford Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NIHR, the NHS or the Department of Health. Conflict of Interest Statement: The authors declare that there are no conflicts of interest. Author Contribution: DSL and COC conceived the idea, designed the analysis, undertook the analysis and wrote the manuscript. NSdH, WJdG, MvdW, JW interpreted data and wrote the manuscript. Data Sharing Statement: Further data is available on request from the corresponding author References. MacMahon S, Peto R, Cutler J, et al. Blood pressure, stroke, and coronary heart disease. Part, Prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. Lancet. 0; (): -.. Muntner P, Shimbo D, Tonelli M, et al. The relationship between visit-to-visit variability in systolic blood pressure and all-cause mortality in the general population: findings from NHANES III, to. Hypertension. 0; (): 0-.. Rothwell PM. Limitations of the usual blood-pressure hypothesis and importance of variability, instability, and episodic hypertension. Lancet. 00; (): -.. Howard SC, Rothwell PM. Reproducibility of measures of visit-to-visit variability in blood pressure after transient ischaemic attack or minor stroke. CerebrovascDis. 00; (): -0.. Muntner P, Joyce C, Levitan EB, et al. Reproducibility of visit-to-visit variability of blood pressure measured as part of routine clinical care. J Hypertens. 0; (): BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

15 BMJ Open Page of Muntner P, Levitan EB, Joyce C, et al. Association Between Antihypertensive Medication Adherence and Visit-to-Visit Variability of Blood Pressure. J ClinHypertens (Greenwich). 0; (): -.. Kawai T, Ohishi M, Kamide K, et al.the impact of visit-to-visit variability in blood pressure on renal function.hypertens Res. 0; (): -.. Yokota K, Fukuda M, Matsui Y, et al.impact of visit-to-visit variability of blood pressure on deterioration of renal function in patients with non-diabetic chronic kidney disease. Hypertens Res. 0; (): -.. Okada H, Fukui M, Tanaka M, et al.visit-to-visit variability in systolic blood pressure is correlated with diabetic nephropathy and atherosclerosis in patients with type diabetes. Atherosclerosis. 0; 0(): Okada H, Fukui M, Tanaka M, et al.visit-to-visit Blood Pressure Variability Is a Novel Risk Factor for the Development and Progression of Diabetic Nephropathy in Patients With Type Diabetes. Diabetes Care. 0.. Mallamaci F, Minutolo R, Leonardis D,et al.long-term visit-to-visit office blood pressure variability increases the risk of adverse cardiovascular outcomes in patients with chronic kidney disease. Kidney Int. 0.. Rossignol P, Cridlig J, Lehert P, et al.visit-to-visit blood pressure variability is a strong predictor of cardiovascular events in hemodialysis: insights from FOSIDIAL. Hypertension. 0; 0(): -.. Go AS, Chertow GM, Fan D, et al.chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 00; (): -0.. Tonelli M, Muntner P, Lloyd A, et al.risk of coronary events in people with chronic kidney disease compared with those with diabetes: a population-level cohort study. Lancet 0;0():0-.. Coresh J, Stevens LA, Levey AS. Chronic kidney disease is common: what do we do next? Nephrol Dial Transplant. 00; (): -.. O'Callaghan CA, Shine B, Lasserson DS. Chronic kidney disease: a large-scale populationbased study of the effects of introducing the CKD-EPI formula for egfr reporting. BMJ Open. 0; (): e Levey AS, Coresh J, Greene T, et al. Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clinical Chemistry. 00; (): -.. Levey AS, Stevens LA, Schmid CH, et al.a new equation to estimate glomerular filtration rate.annals of Internal Medicine.00; 0(): BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

16 Page of BMJ Open Coresh J, Stevens LA, Levey AS. Chronic kidney disease is common: what do we do next? Nephrol Dial Transplant. 00; (): Di Iorio B, Pota A, Sirico ML, et al.blood pressure variability and outcomes in chronic kidney disease. Nephrol Dial Transplant. 0; (): National Coordinating Centre for Chronic Conditions. CHRONIC KIDNEY DISEASE.National clinical guideline for early identification and management in adults in primary and secondary care. London: Royal College of Physicians; 00.. De Grauw WJC, Kaasjager HAH, Bilo HJG,et al. Landelijketransmuraleafspraakchronischenierschade. Huisarts Wet. 00; (): -.. Rothwell PM, Howard SC, Dolan E, et al. Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet. 00; (): -0.. Webb AJ, Rothwell PM. Effect of dose and combination of antihypertensives on interindividual blood pressure variability: a systematic review. Stroke. 0; (0): BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

17 BMJ Open Page of Figure legend Figure. Renal function and age both associated with blood pressure variability. Colors indicate the number of data points in each shaded area of the curve. The regression line is shown. A. The relationship between standard deviation independent of mean (SDIM) and egfr. B. The relationship between SDIM and age. Data frequency contours are shown for clarity. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

18 Page of BMJ Open Figure. Renal function and age both associated with blood pressure variability. Colors indicate the number of data points in each shaded area of the curve. The regression line is shown. A. The relationship between standard deviation independent of mean (SDIM) and egfr. B. The relationship between SDIM and age. Data frequency contours are shown for clarity. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

19 BMJ Open Page of Supplementary Information Formulae for variability metrics Different methods to measure variability have been used in the literature with the following being adopted by researchers in this field. Successive variation (SV) = ( ( Where for each patient is the ith measure for I =,.n Absolute Real Variation (ARV) = Where for each patient is the ith measure for I =,.n Correlations between mean BP and measures of variability The following transformations have been used to reduce the influence of mean BP on measures of variability thereby reducing the confounding from BP in associations between BP variability and cardiovascular events. SD independent of mean (SDIM) SD = Where k and x are found by curve fitting for the entire patient cohort from a plot of SD versus mean BP (separately for systolic and diastolic), and then these parameters applied to individual patient mean BP and SD to create the SDIM statistic. SV independent of mean (SVIM) SV = Where k and x are found by curve fitting for the entire patient cohort from a plot of SV versus mean BP (separately for systolic and diastolic), and then these parameters applied to individual patient mean BP and SV to create the SVIM statistic. Supplementary Page - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

20 Page of BMJ Open ARV independent of mean (ARVIM) = ARV Where k and x are found by curve fitting for the entire patient cohort from a plot of ARV versus mean BP (separately for systolic and diastolic), and then these parameters applied to individual patient mean BP and SV to create the SVIM statistic. The same approach is applied to the absolute real variation (ARV) to generate the ARVIM statistic. Supplementary Table. Standardised beta coefficients and associated p values for drug classes as covariates in multivariable linear regression of egfr on measures of BP variability BP measure ACEI/ARB Beta Thiazide like Calcium channel Mineralocorticoid blockers diuretics blockers receptor blockers Maximum systolic 0.0 *** 0.0 ** 0.0 * 0.00 * 0.00 * BP Absolute real 0.0 *** 0.0 *** 0.0 ** variation Successive 0.0 *** 0.0 *** 0.0 ** 0.0 * variation Standard 0.0 *** 0.0 *** 0.0 *** 0.0 ** 0.0 *** deviation of systolic BP Standard 0.0 *** 0.0 *** 0.0 ** 0.0 * deviation independent of mean Absolute real 0.0 *** 0.0 *** 0.0 ** 0.0 ** variation independent of mean Successive variation independent of mean 0.0 *** 0.0 *** 0.0 *** 0.0 ** 0.0 *** * P<0.0, ** P<0.0, *** P<0.00 Supplementary Page - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

21 BMJ Open Page 0 of Section/Topic Item # STROBE 00 (v) Statement Checklist of items that should be included in reports of cohort studies Recommendation Reported on page # Title and abstract (a) Indicate the study s design with a commonly used term in the title or the abstract Introduction (b) Provide in the abstract an informative and balanced summary of what was done and what was found Background/rationale Explain the scientific background and rationale for the investigation being reported Objectives State specific objectives, including any prespecified hypotheses Methods Study design Present key elements of study design early in the paper Setting Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection Participants (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up / (b) For matched studies, give matching criteria and number of exposed and unexposed Variables Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if Data sources/ measurement applicable * For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group Bias Describe any efforts to address potential sources of bias / Study size 0 Explain how the study size was arrived at Quantitative variables Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why Statistical methods (a) Describe all statistical methods, including those used to control for confounding Results on November 0 by guest. Protected by copyright. (b) Describe any methods used to examine subgroups and interactions (c) Explain how missing data were addressed (d) If applicable, explain how loss to follow-up was addressed (e) Describe any sensitivity analyses - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from NA /

22 Page of BMJ Open Participants * (a) Report numbers of individuals at each stage of study eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed (b) Give reasons for non-participation at each stage Retrospective cohort Descriptive data (c) Consider use of a flow diagram * (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential from routine data Retrospective cohort from routine data confounders (b) Indicate number of participants with missing data for each variable of interest (c) Summarise follow-up time (eg, average and total amount) Outcome data * Report numbers of outcome events or summary measures over time Main results (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, % confidence / interval). Make clear which confounders were adjusted for and why they were included (b) Report category boundaries when continuous variables were categorized No categorization (c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period Not relevant Other analyses Report other analyses done eg analyses of subgroups and interactions, and sensitivity analyses / Discussion Key results Summarise key results with reference to study objectives 0 Limitations Interpretation 0 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence Generalisability Discuss the generalisability (external validity) of the study results / Other information Funding Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based *Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies. Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at Annals of Internal Medicine at and Epidemiology at Information on the STROBE Initiative is available at BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

23 BMJ Open What is the relationship between renal function and visitto-visit blood pressure variability in primary care? Retrospective cohort study from routinely collected healthcare data. Journal: BMJ Open Manuscript ID bmjopen r Article Type: Research Date Submitted by the Author: 0-Mar-0 Complete List of Authors: Lasserson, Daniel; Oxford University Hospitals NHS Foundation Trust, NIHR Oxford Biomedical Research Centre; University of Oxford, Nuffield Department of Medicine Scherpbier de Haan, Nynke; Radboud University Nijmegen Medical Centre, Primary and Community Care de Grauw, Wim; Radboud University Nijmegen Medical Centre, Primary and Community Care van der Wel, Mark; Radboud University Nijmegen Medical Centre, Primary and Community Care Wetzels, Jack; Radboud University Nijmegen Medical Centre, Nephrology O'Callaghan, Christopher; University of Oxford, Nuffield Department of Clinical Medicine; Oxford Radcliffe Hospitals Trust, John Radcliffe Hospital <b>primary Subject Heading</b>: Renal medicine Secondary Subject Heading: General practice / Family practice, Cardiovascular medicine Keywords: Blood Pressure, Chronic kidney disease, Variability BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

24 Page of BMJ Open What is the relationship between renal function and visit-to-visit blood pressure variability in primary care? Retrospective cohort study from routinely collected healthcare data. Daniel S. Lasserson MBBS MD,, Nynke Scherpbier de Haan MD PhD, Wim de Grauw MD PhD, Mark van der Wel MD PhD, Jack F. Wetzels MD PhD, Christopher A. O Callaghan MB BChir DM DPhil Nuffield Department of Medicine, University of Oxford, UK NIHR Oxford Biomedical Research Centre, Department of Geratology, Oxford University Hospitals NHS Trust, Oxford, UK Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, Netherlands Department of Nephrology, Radboud University Nijmegen Medical Centre, Netherlands Corresponding author: Professor Christopher O Callaghan, Henry Wellcome Building for Molecular Physiology, Nuffield Department of Medicine, Roosevelt Drive, University of Oxford, Oxford OX B Tel + Fax + chris.ocallaghan@ndm.ox.ac.uk Word Count 0 Keywords Blood pressure, chronic kidney disease, variability. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

25 BMJ Open Page of ABSTRACT Objective: To determine the relationship between renal function and visit to visit blood pressure variability in a cohort of primary care patients. Design: Retrospective cohort study from routinely collected healthcare data. Setting: Primary care in Nijmegen, Netherlands from Participants:, patients who had a measure of renal function and seven separate visits with blood pressure readings in the primary care record Outcome measures: Visit-to-visit variability in systolic blood pressure (BP), calculated from the first seven office measurements, including standard deviation, successive variation, absolute real variation and metrics of variability shown to be independent of mean. Multiple linear regression was used to analyze the influence of estimated glomerular filtration rate (egfr) on BP variability measures with adjustment for age, sex, diabetes, mean BP, proteinuria, cardiovascular disease, time interval between measures and antihypertensive use. Results: In the patient cohort, % were female, mean (SD) age was. (.) years, mean (SD) egfr was. (.0) ml/min/.m and mean (SD) systolic BP. (.) mmhg. All BP variability measures were negatively correlated with egfr and positively correlated with age. However, multiple linear regression demonstrated consistent, significant negative relationships between egfr and all measures of BP variability adjusting for confounding variable. Conclusion: Worsening renal function is associated with small increases in measures of visit-to-visit BP variability after adjustment for confounding factors. This is seen across the spectrum of renal function in the population and provides a mechanism whereby CKD may raise the risk of cardiovascular events. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

26 Page of BMJ Open Article Summary Strengths and limitations of this study We studied BP variability in a patient cohort generalizable to primary care where the majority of patients with CKD are diagnosed and monitored as previously BP variability has only been studied in patients with advanced renal disease under specialist care. We examined measures of BP variability that are independent of mean BP, which have been shown to have greater prognostic significance than mean BP alone. We were not able to assess the effect of different exposures to antihypertensive medication. We did not have access to data on cardiovascular outcomes to test the clinical significance of visit-to-visit variability in this patient cohort. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

27 BMJ Open Page of Introduction Hypertension is a major vascular risk factor and average blood pressure (BP) correlates with cardiovascular disease and death. Recently, visit-to-visit variability in BP measurements has been shown to convey an additional independent risk for cardiovascular events and to be associated with all-cause mortality in the general population., BP variability is reproducible within individuals over time in clinical trials and in observational data from routine care. The cardiovascular prognostic significance of BP variability is increasingly recognized and the variability is not explained by poor medication adherence. Increased BP variability has been observed in small groups of patients with relatively advanced renal disease under specialist hospital care. In a small hospital cohort, visit-to-visit BP variability was independently associated with albuminuria and with increased renal vascular resistance as assessed by Doppler ultrasound. A study of patients with non-diabetic chronic kidney disease (CKD) indicated a correlation between indices of BP variability and the rate of decline in renal function. In type diabetes, visit-to-visit variability in systolic BP correlates with the level of albuminuria, ankle brachial pressure index and the probability of developing new albuminuria. 0 In a set of secondary care patients with substantial renal impairment, there was a correlation between BP variability and a composite outcome of death and cardiovascular events. In patients on haemodialysis, visit-to-visit variability in BP is an independent predictor of vascular events, but this is complicated by the dependence of BP in this context on intradialytic fluid gain. However, as BP variability increases with mean BP and renal disease is associated with increased mean BP, the relationship between BP variability and renal impairment can only be established using metrics of BP variability that have been shown to be independent of mean BP. None of the renal studies above reported statistics of BP variability that were demonstrated to be independent of the - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

28 Page of BMJ Open mean BP and, therefore, an evidence gap remains concerning whether declining renal function is associated with BP variability. It is important to establish whether there is a real relationship between renal impairment and increased BP variability because the link might be causal and have important implications for therapy in CKD. Analysis of large sets of routinely collected healthcare data has demonstrated that CKD is an important independent risk factor for cardiovascular disease even after controlling for other known risk factors., The prevalence of CKD is around -0 % and the majority of patients with CKD are managed by non-specialists or primary care physicians., Given that a substantial proportion of the population have early stage CKD, it is therefore crucial to establish whether early stage CKD is associated with increased BP variability, as there could be major implications for population cardiovascular risk factor monitoring and treatment. We therefore sought to determine whether renal function was associated with visit-to-visit BP variability using a large dataset of routinely collected healthcare data. Methods We retrospectively defined a patient cohort (age years) using routinely collected healthcare data from primary care practices in the Nijmegen region, the Netherlands, with a combined registered population of 0, people, as part of the CONTACT study (Consultation of Nephrology by Telenephrology Allows optimal Chronic kidney disease Treatment in primary care), Netherlands Trial Registration code with approval from the Medical Research Ethics Committee Arnhem/Nijmegen registration number 00/. Data included demographic details, medical history with conditions diagnosed in the course of routine clinical practice,bp measurements, antihypertensive prescribing and renal function between st January 00 and st January 0. We did not seek to include patients receiving dialysis in this - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.

29 BMJ Open Page of study population. The CKD-EPI formula was used to calculate estimated glomerular filtration rates (egfrs) using the first creatinine measurements in the time period, which were either standardized to isotope dilution-mass spectrometry (IDMS) or subject to the appropriate correction factor for laboratories using the Jaffé technique. CKD-EPI egfrs have been shown to correlate better with measured GFRs than egfrs obtained with the MDRD formula. The first seven office BP measurements were used to calculate variability metrics as this approach has been shown to optimize reproducibility. Dutch primary care physicians and practice nurses have free access to the Dutch College of General Practitioners guideline on cardiovascular risk management for a clear description of standardised office BP measurement, which is in agreement with international standards. Given that blood pressure variability is associated with mean BP, we calculated BP variability independent of mean BP using standard formulae for measures that have prognostic significance for cardiovascular events. For the systolic and diastolic BP of individuals with at least seven BP measures we calculated the standard deviation (SD), successive variation (SV), average real variability (ARV) and transformed these to be independent of mean BP (see supplementary information for formulae). Observed associations between renal functionand BP variability (systolic BP metrics described above and also including maximum systolic BP) may be confounded by other predictors of variability and we therefore tested the association between renal function and measures of BP variability using multiple stepwise linear regression adjusting for age, sex, mean BP, diabetes, history of cardiovascular disease (including vascular events, arrhythmia and heart failure), proteinuria, time interval between BP measures and class of antihypertensive drug prescribed. We included mean BP as a covariate to ensure that the effect of mean BP was truly adjusted for in regressions testing the relationship between variability independent of mean (VIM) statistics and egfr. Analyses were carried out using R (version.0.) and SPSS version. - BMJ Open: first published as 0./bmjopen on 0 June 0. Downloaded from on November 0 by guest. Protected by copyright.